WO2022021957A1

WO2022021957A1 - Two-stage stochastic programming-based v2g scheduling model for maximizing operator revenue

Info

Publication number: WO2022021957A1
Application number: PCT/CN2021/088841
Authority: WO
Inventors: 黄玉萍; 胡晨
Original assignee: 中国科学院广州能源研究所
Priority date: 2021-03-16
Filing date: 2021-04-22
Publication date: 2022-02-03
Also published as: CN115081777A; US20240185150A1; CN115081777B

Abstract

A two-stage stochastic programming-based V2G scheduling method for maximizing operator revenue, relating to the field of energy management optimization models. Said method aims for the charge/discharge scheduling problem of electric vehicles, and establishes, on the basis of a distributed renewable energy-storage-EVs charge/discharge power system, a V2G two-stage nonlinear stochastic programming model combining the V2G scheduling randomness with the renewable energy power generation randomness. Said model is converted into a mixed integer linear programming model (MILP) by means of constraint linearization. In addition, in order to enable random scenarios to cover a plurality of uncertainty factors comprehensively, a scenario generation and combination method is designed to combine the V2G scheduling resources with the randomness of the renewable energy level. The V2G two-stage stochastic programming model solves an optimal charge/discharge plan of the electric vehicles seeking to adapt the randomness of the V2G scheduling layer and the renewable energy randomness, and increases the revenue of said model participating in power assistance services.

Description

V2G two-stage stochastic planning scheduling model for operator revenue maximization

technical field

The invention relates to the field of energy management optimization models, in particular to a V2G scheduling method based on two-stage stochastic planning for maximizing operator benefits.

Background technique

V2G, short for Vehicle-to-Grid, is designed for electric vehicles to interact with the grid, using the electric vehicle's battery as a buffer for the grid and renewable energy. In the external environment of energy conservation and emission reduction and the shortage of fossil energy, electric vehicles (EVs) are gradually occupying more fuel vehicle markets due to their low operating costs and outstanding energy conservation and environmental protection effects. In addition to energy conservation and emission reduction, EVs, as mobile energy storage, interact with the power grid through V2G, which can bring many auxiliary services to the power grid, including auxiliary peak regulation and auxiliary frequency regulation for the power grid. This model can realize auxiliary peak regulation, and can accurately control the charging and discharging state of EVs and the charging and discharging capacity of EVs, so that EVs can participate in grid operation regulation in an orderly manner. When EVs participate in the regulation of power grid operation, the centralized dispatch of V2G operators (dispatching center, AG) is indispensable.

The V2G operator is the main revenue body of the model, and its functions include: managing the charging and discharging of EVs within the agreement, providing power for EVs outside the agreement, operating the renewable energy power generation system in the region, providing power transfer for regional partial loads and carrying out regional surplus power go online.

The problem of EVs participating in V2G charging and discharging scheduling is an optimal decision-making problem with multiple uncertainties. Uncertainty can be divided into V2G scheduling resource randomness and renewable energy generation randomness. In previous studies, it is difficult to comprehensively consider the multiple randomness of EVs participating in the V2G process, and the research on the combination of V2G scheduling resources and the randomness of renewable energy is not in-depth.

SUMMARY OF THE INVENTION

In view of the deficiencies in the prior art, the present invention provides a V2G scheduling method based on two-stage stochastic programming that maximizes the operator's income, and a V2G two-stage nonlinear stochastic programming model combining the randomness of V2G scheduling and the randomness of renewable energy generation. , which combines V2G scheduling resources with randomness at the level of renewable energy.

For achieving the above object, technical scheme of the present invention is as follows:

A V2G dispatching two-stage stochastic planning method for maximizing the operator's income, which is used in a system including at least electric vehicles, charging and discharging piles and a power grid, which includes the following steps:

Obtain the day-ahead parameter set of the electric vehicles in the operator's service area, and at the same time, issue a dispatching invitation agreement to the electric vehicles in the operator's service area, and classify the electric vehicles that agree to the dispatching invitation agreement as the electric vehicles in the agreement, and no response and Electric vehicles that refuse the dispatching invitation agreement are regarded as electric vehicles outside the agreement;

A random scenario set is constructed based on the day-ahead parameter set of electric vehicles in the operator's service area, the conditions of in-protocol electric vehicles and out-of-protocol electric vehicles, and the power generation of renewable energy. Under the random charging requirements of external electric vehicles, the charging and discharging optimization scheduling of electric vehicles within the agreement is carried out;

Considering that each random factor is independent of each other, a final random scenario is constructed by using the random scenario ensemble model, and a V2G two-stage nonlinear stochastic programming model under the final random scenario is constructed;

The V2G two-stage nonlinear stochastic programming model is used to maximize the overall revenue of the V2G operator.

Compared with the prior art, the present invention has the following beneficial effects:

Fully considering the uncertainty of V2G dispatching resources and renewable energy generation, a two-stage stochastic programming model is established to maximize the operator's revenue, effectively improving the V2G dispatching process, clarifying and quantifying the revenue source of the V2G dispatching system, and comprehensively optimizing the electric power in the agreement. The operation status of vehicles participating in V2G scheduling provides theoretical and methodological support for the establishment of the optimal utilization of vehicle-network interaction resources.

Considering that electric vehicles are affected by a variety of randomness, the scenario generation method for the randomness of V2G dispatching resources and the randomness of renewable energy generation is improved, so that the scenario set of the two-stage stochastic programming model fully reflects a variety of random factors. .

Description of drawings

In order to illustrate the technical solutions in the embodiments of the present invention more clearly, the following will briefly introduce the accompanying drawings that need to be used in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can also be obtained from these drawings without any creative effort.

1 is a flowchart of a method for V2G scheduling in an embodiment of the present invention;

FIG. 2 is a benefit-cost relationship diagram of a V2G operator in an embodiment of the present invention;

3 is a schematic diagram of a scenario generation process considering randomness V2G optimal scheduling model in an embodiment of the present invention;

FIG. 4 is a distribution diagram of V2G operator network nodes in an embodiment of the present invention;

Fig. 5 is the EVs decision variable diagram in the embodiment of the present invention;

FIG. 6 is a bar graph of the charging and discharging load of EVs in a scenario according to an embodiment of the present invention.

detailed description

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

Example:

It should be noted that the terms "comprising" and "having" and any modifications thereof in the embodiments of the present invention are intended to cover non-exclusive inclusion, for example, a process, method, system, product or process including a series of steps or units. The apparatus is not necessarily limited to those steps or units expressly listed, but may include other steps or units not expressly listed or inherent to the process, method, product or apparatus.

In a specific embodiment, the present invention may include the following steps:

Step 1. V2G vehicle pile network resource monitoring statistics

Statistics and analysis of EVs and service station capacity in the service area of V2G operators are used to construct random scenario sets:

1. Vehicle-pile-network information interaction, real-time data update, and access to the day-to-day parameters of vehicles participating in scheduling (model, battery capacity, battery power, parking location, charging and discharging climbing ability, etc.).

2. According to the results of the user's response to the grid V2G dispatch invitation, EVs that agree to participate in the previous dispatch are classified as EVs within the agreement, and EVs that do not respond and refuse the invitation are regarded as EVs outside the agreement.

3. EVs in the agreement: The vehicles that have promised to participate in the dispatching are arranged at the charging and discharging stations managed by the operator according to the principle of distance optimization, connect to the grid before the specified time, and respond to the charging and discharging, and off-grid instructions of the dispatching center in real time.

4. EVs outside the protocol: randomly generated charging requirements, which are preferentially satisfied by the dispatching center. Such charging requirements are not controlled by the dispatching center.

Step 2. Random Scenario Generation - Combination

V2G operators generate random scenarios through scenario generation-combination method, which are applied to the second-stage constraints of the V2G scheduling mathematical model. Under the condition of meeting the random charging requirements of EVs outside the agreement, the electric vehicles within the agreement are optimally scheduled for charging and discharging. 2-1. V2G Scheduling Resource Random Scenario

The random scenarios of V2G scheduling resources mainly include random scenarios of vehicle initial SOC and random scenarios of V2G service station resources.

In order to show the SOC randomness of EVs participating in scheduling in the protocol, the log-normal distribution model (1) of the daily driving distance of electric vehicles in the protocol is adopted, and the driving distance of EVs in the protocol before grid connection is obtained by the Monte Carlo method, which is regarded as the random driving distance. parameter

The corresponding generated random scenario set is SC ^D .

To describe the randomness of the schedulable resources (schedulable capacity) of EVs charging and discharging stations. Select a homogeneous Poisson model with a constant average arrival rate to describe the random arrival number of out-of-protocol EVs (2), and obtain the number of out-of-protocol EVs randomly arriving at the charging and discharging station by Monte Carlo method as the random parameter of arrival quantity

Then generate a random scenario set SC ^Z accordingly.

2-2. Stochastic scenario of renewable energy power generation

According to the Weibull distribution of wind power generation (3), the random parameters of wind speed are obtained by Latin hypercube sampling (LHS).

The number of scenarios is reduced by simultaneous backward reduction, and finally the wind power output scenario SC ^WT is generated by the wind turbine power fitting model.

In terms of photovoltaic power generation simulation, one year's historical data of photovoltaic daily power generation is selected to establish a photovoltaic power generation scenario pool, random scenarios of photovoltaic power generation are obtained through random sampling, and random scenarios of photovoltaic power generation are generated by synchronous backward reduction method. Scenario SC ^PV .

2-3. Combinations of random scenarios

Considering that each random factor is independent of each other, the above four types of random scenarios (SC ^D , SC ^Z , SC ^WT , SC ^PV ) are combined and calculated. According to formula (4), the above random scenarios are cross-combined to generate the final random scenario SC ^F of the model . Equation (4) is used to calculate the probability of scenario combination SC ^F.

where P(sc ^D ) is 1/SC ^D , respectively, and P(sc ^Z ) is 1/SC ^Z , respectively. P(sc ^PV ) and P(sc ^WT ) are determined by the scenario reduction algorithm.

Step 3. V2G two-stage nonlinear stochastic programming model

First, define variables for the process of electric vehicles participating in scheduling, and define variables for the energy supply of regional microgrids. Then the revenue framework of the V2G operator is built, and then the second-stage constraints are established and the constraints are linearized.

Table 1-Table 3 lists the parameters and variables of the V2G two-stage nonlinear stochastic programming model, which are defined as follows.

Table 1 Indexes and collections

Table 2 Scheduling model parameters

Table 3 Model variables

3-1. Objective function

The objective equation F maximizes the overall revenue of the V2G operator, see equation (5). Among them, Equation (6) is the operator’s total revenue (Rev _EV ) in which electric vehicles participate in dispatching; Equation (7) is the operator’s total revenue (Rev _AG ) from coordinating power supply to local loads and surplus power grid; Equation (8) is The operator purchases the total cost of thermal power on the day before and on the current day (Cost _B ); Equation (9) is the total cost of renewable energy generation under the jurisdiction of the operator (Cost _OM ).

where:

The first stage constraints:

Equation (10) is the repulsion constraint of electric vehicle charging and discharging: the charging operation and discharging operation of the same electric vehicle cannot occur at the same time during the scheduling period;

Equation (11-14) is the electric vehicle charging state constraint: the electric vehicle is connected to the distribution network to start charging within the t period, in order to limit the shortest charging time and the shortest idle time, so as to avoid frequent switching between charging, discharging and idle states, resulting in Damaged EV batteries and higher costs of switching services; of which

for the shortest charging time,

is the shortest idle time;

Equation (15-18) electric vehicle discharge state constraint: used to limit the shortest discharge time and the shortest idle time; where

is the shortest discharge time;

Equation (19-21) Constraints on the maximum switching times of electric vehicle charging and discharging: the maximum number of electric vehicle charging and discharging switching times in a day is limited, and the maximum number of electric vehicle switching states in a day can be limited, which can effectively avoid excessive state switching of electric vehicles frequently; of which

are the upper limit of the charging and discharging times of a single electric vehicle in the V2G scheduling plan, respectively, and V _i is the upper limit of the switching times of charging and discharging;

The second stage constraints:

Equation (22-23) The initial state constraints when electric vehicles are connected to the grid: Equation (22) calculates the initial power when the vehicle is connected to the grid through the travel distance before the electric vehicle is connected to the grid, and Equation (23) calculates the initial SOC of EV _i , driving The randomness of the distance causes the initial SOC of the electric vehicle cluster to be random; where

is a random parameter of EV _i ’s driving distance before grid connection in the protocol;

Equation (24-26) is the constraint on the maximum number of services with V2G nodes: due to the limitation of V2G service station capacity and transformer power, the number of vehicles that can be charged and discharged at the same node is limited; Equation (24) limits the maximum number of nodes that can be charged at the same time. Equation (25) limits the maximum number of electric vehicles that can be discharged at the same time at node m, and Equation (26) limits the number of electric vehicles that can be charged and discharged at the same time at node m less than the number of charging and discharging piles; where α _m , β _m are each The maximum number of vehicles that can be charged and discharged during the V2G service station period;

Equations (27-28) are the electric vehicle charging and discharging energy constraints: during the charging and discharging process of the electric vehicle, the actual chargeable and dischargeable amount is limited by the real-time SOC; among them: when

and

When both are 0, EV _i is charged at time period t

and discharge capacity

is constrained to 0; when

or

, the charging capacity of EV _i

and discharge capacity

Respectively subject to the maximum value of the schedulable capacity of the battery

and

constraint;

Equation (29-30) is the state-of-charge constraint of the electric vehicle: the variation range of the battery state of charge of the electric vehicle in the scheduling protocol is given; Equation (29) is the optimal battery operating condition range when the vehicle participates in V2G; Equation (30) It means that the state of charge SOC of the electric vehicle needs to meet the user's expectation after the scheduling, and the charging and discharging scheduling is carried out on the premise of meeting the user's future travel needs; where T _end is set as the scheduling end time.

Equation (31) is the electric vehicle power balance constraint: the electric power of EV _i in the t period is equal to the remaining electric power in the t-1 period plus the difference between the charge and discharge in the t period;

Equation (32-33) Electric vehicle charging and discharging climbing constraints: the charging and discharging climbing ability of electric vehicles is affected by the rated power of the charging and discharging piles and the charging mode. climb

Avoid aggravated capacity loss caused by over-charging and discharging of batteries; if and only after the vehicle accepts the first-stage scheduling plan, the charge-discharge climbing constraint will take effect in the second stage;

The maximum hill-climbing capability for charging is,

The maximum ramping capability for discharge is;

Equations (34-35) are the maximum service capacity constraints of V2G nodes: Equation (35) calculates the total amount of charging demand generated by electric vehicles that arrive randomly outside the protocol; Equation (34) The capacity of electric vehicles in the protocol that can participate in charging scheduling, this part The amount of electricity is affected by the number of electric vehicles and the amount of charging outside the agreement of formula (35) and is random; among them

is the random arrival number of electric vehicles outside the agreement,

The rated charging capacity that node m can provide;

where:

Equations (36-37) are the network node capacity and balance constraints: the model builds an energy transmission network, and the node power balance of the network satisfies Kirchhoff’s law; Equation (36) limits the maximum capacity of the bidirectional energy flow, and stipulates that the power transmission is in the standard inside; formula (37) is introduced in

After describing the randomness of wind and solar power generation, construct an energy balance constraint for each node to ensure that the total inflow of the node is equal to the total outflow;

Linearization with nonlinear constraints:

Since both constraint equations (27) and (28) have nonlinear terms, equation (27) is transformed into equations (38)-(40), and equation (28) is transformed into equations (41)-(43) in the same way, In order to improve the quality and speed of the model solution set, where,

Based on the above objectives and constraints, the two-stage stochastic optimization model of wind-solar power generation randomness and V2G resource randomness is constructed as follows.

Maxmize F (5)

Subject to:

First-stage Constraints:(10)-(21)

Charge and Discharge Repulsion Constraints (10)

Charge state constraints (11)-(14)

Discharge state constraints (15)-(18)

Constraints on maximum switching times of charge and discharge (19)-(21)

Second-stage Constraints:(22)-(43)

Initial state constraints when EVs are connected to the grid (22)-(23)

V2G node maximum number of services constraints (24)-(26)

EVs charge and discharge energy constraints (27)-(28)

Linearization of EVs charge and discharge energy constraints (38)-(43)

EVs state of charge constraints (29)-(30)

EVs power balance constraints (31)

EVs charge and discharge ramp constraints (32)-(33)

V2G Node Maximum Capacity Constraints (34)-(35)

Network Node Capacity and Balance Constraints (36)-(37)

The following is a detailed description of the model optimization results combined with specific examples:

Taking the area managed by the V2G operator combined with the transaction mechanism of the renewable energy power generation system as the background, the standard distribution network topology of IEEE-33 nodes is selected, and some nodes are pre-installed with wind turbines and photovoltaic power generation systems. The topology is shown in Figure 4. .

In order to verify the optimization effect of the model, the following parameters are first designed, 8 V2G nodes are set, and 100 EVs in the scheduling protocol are charged and discharged in an orderly manner. The model and some parameters are shown in Table 4:

Table 4 Participating in scheduling EVs parameters

Nodes

20 and 11 are respectively equipped with a single GE1.5-77 wind turbine and a GE1.7-100 high-power wind turbine, and other V2G service sites are equipped with small wind power photovoltaic systems. parameter.

Table 5 Fan parameters

For intra-protocol EVs, the set target SOC for the end of scheduling is 0.8. The charging power of EVs arriving randomly outside the protocol is set to be 40kw.

Multivariate joint scenarios were generated, setting SC ^D to 4, SC ^Z to 5, SC ^WT to 5, and SC ^PV to 2. ^SCF = 200 final scenarios were generated by scenario combination. In the Python environment, the branch and bound algorithm of the Gurobi solver is called to solve the model.

Figure 5 shows the EVs charging and discharging decision diagram under the constraints of the shortest charging and discharging time. It shows that almost all EVs in the protocol participate in the charging and discharging day scheduling, and the way of participating in the scheduling obeys the model constraints. The proportion of the dispatched time period for EVs clusters is close to 100% of the total time period, which proves that the model can effectively control the charging and discharging status of electric vehicles in the protocol, and under the goal of maximizing the revenue of the dispatch center, the EVs clusters in the protocol need to be on standby all day and keep connected state. The period from 0:00 to 2:00 in the morning is the concentrated discharge period of EVs. Because the discharge price during this period is relatively low, EVs are discharged in the dispatching agreement of the dispatch center to supply other loads to maximize dispatching revenue.

Figure 6 proves the random parameters of EVs driving distance before grid connection

Influence on charge and discharge load. The EVs cluster in the protocol is constrained by the relationship between supply and demand, and the discharge is more concentrated between 0:00-5:00 in the morning and 8:00-10:00 in the morning. random parameters

It has an impact on the discharge load during the period of 0:00-5:00 in the morning. The greater the average value of the driving distance before the day, the lower the SOC at the time of access, and the smaller the load participating in the discharge. But the discharge load from 8:00-10:00 in the morning is not subject to random parameters

effect, because the random parameter

Only affects the initial SOC of EVs. After charging during the period of 5:00-7:00 in the morning, the random parameter

impact has been eliminated. During the period from 17:00 to 23:00 in the evening, in order to achieve the target SOC=0.8 at the end of the dispatch day, the charging load of EVs will increase significantly.

The optimization results of the objective function in Table 6 show that when 100 EVs are dispatched, the expected revenue of the dispatch center on this dispatch day is 69,323.4 yuan. Among them, the charging income of dispatching EVs is 12,359.5 yuan, the discharging cost of dispatching EVs is 3,388.9 yuan, and the net income of dispatching EVs is 8,970.6 yuan. The profit from dispatching EVs accounts for 13% of the total profit. The biggest profit of the dispatch center comes from the local load power consumption. Through the local consumption of renewable energy to supply the regional power load and electric vehicles, the dispatch center can obtain considerable benefits.

Table 6 Objective function energy scheduling benefit table

In the description of this specification, description with reference to the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples", etc., mean specific features described in connection with the embodiment or example , structure, material or feature is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, those skilled in the art may combine and combine the different embodiments or examples described in this specification, as well as the features of the different embodiments or examples, without conflicting each other.

The above-mentioned embodiments are only to illustrate the technical concept and characteristics of the present invention, and the purpose thereof is to enable those of ordinary skill in the art to understand the content of the present invention and implement them accordingly, and not to limit the protection scope of the present invention. All equivalent changes or modifications made according to the essence of the present invention shall be included within the protection scope of the present invention.

Claims

A V2G scheduling method based on two-stage stochastic planning that maximizes the operator's income, which is used in an energy system comprising at least electric vehicles, charging and discharging piles and a power grid, and is characterized in that it includes the following steps:

Obtain the day-ahead parameter set of the electric vehicles in the operator's service area, and at the same time, issue a dispatching invitation agreement to the electric vehicles in the operator's service area, and classify the electric vehicles that agree to the dispatching invitation agreement as the electric vehicles in the agreement, and no response and Electric vehicles that reject the dispatch offer agreement are classified as out-of-agreement electric vehicles;

Based on the day-ahead parameter set of electric vehicles in the operator's service area, the conditions of electric vehicles in the agreement and electric vehicles outside the agreement, a random scenario set is constructed. Optimal scheduling of charging and discharging of internal electric vehicles;

Considering that each random factor is independent of each other, a random scenario of V2G scheduling resources and renewable energy power generation is generated in combination, a final random scenario is constructed, and a V2G two-stage stochastic nonlinear planning and dispatching model is constructed under the final random scenario;

The V2G two-stage stochastic nonlinear programming scheduling model is solved to maximize the overall revenue of the V2G operator.
The V2G scheduling method based on two-stage stochastic planning for maximizing operator revenue according to claim 1, wherein the constructing a random scenario set includes a V2G scheduling resource random scenario, and the V2G scheduling resource random scenario includes one of the following: One or more random scenarios, such as random scenarios of initial SOC, random scenarios of V2G service station resources, scenarios of power supply side or demand side load uncertainty, where,

Random scenario of initial SOC: In order to show the randomness of SOC when electric vehicles in the protocol participate in scheduling, the log-normal distribution model (1) of the distance traveled by electric vehicles in the protocol is used, and the Monte Carlo method is used to obtain the pre-connection of electric vehicles in the protocol. The driving distance of , as the driving distance random parameter
The corresponding generated random scenario set is SC D ;

Random scenario of V2G service station resources: In order to show the randomness of the available charging and discharging pile resources in the V2G service station, since the V2G service station can serve electric vehicles within the agreement and outside the agreement at the same time, a homogeneous Poisson model with a constant average arrival rate is used to describe The number of random arrivals of EVs outside the protocol (2), the number of EVs outside the protocol that randomly arrives at the charging and discharging station is obtained by the Monte Carlo method as a random parameter for the number of arrivals
Then generate a random scenario set SC Z accordingly.
The V2G scheduling two-stage stochastic planning method for maximizing operator revenue according to claim 2, wherein the constructing the stochastic scenario set further comprises a wind power generation stochastic scenario and a photovoltaic power generation stochastic scenario, wherein,

In the stochastic scenario of wind power generation: according to the wind power generation Weibull distribution model (3), the random parameters of wind speed are obtained through Latin hypercube sampling;

Reduce the number of scenarios by simultaneous backward reduction, and finally generate the wind power output scenario SC WT through the wind turbine power fitting model;

In the random scenario of photovoltaic power generation, one year of historical data of daily photovoltaic power generation is selected to establish a photovoltaic power generation scenario pool, and random scenarios of photovoltaic power generation are obtained through random sampling, and the random scenario SC PV is generated by the synchronous back-to-back scenario reduction method.
The V2G scheduling method based on two-stage stochastic planning for maximizing operator revenue according to claim 3, wherein,

Considering that each random factor is independent of each other, in order to calculate the above four types of random scenarios SC D , SC Z , SC WT , and SC PV , the above random scenarios are cross-combined to generate the final random scenario SC F ; Equation (4) is used to calculate probability of scenario combination SC F ;

Among them, P(sc D ) is 1/SC D , and P(sc Z ) is 1/SC Z , respectively; P(sc PV ) and P(sc WT ) are determined by the scenario reduction algorithm.
The V2G scheduling method based on two-stage stochastic programming for maximizing operator revenue according to claim 4, wherein the V2G two-stage stochastic nonlinear programming model includes an objective function, a first-stage constraint and a second-stage Restrictions.

Objective function: The objective equation F maximizes the overall revenue of V2G operators, as shown in equation (5); where, equation (6) is the operator’s total revenue (Rev EV ) that electric vehicles participate in dispatching; equation (7) is the operator’s coordinated power The total revenue (Rev AG ) of supplying local loads and remaining power grid; formula (8) is the total cost of thermal power purchased by the operator on the day before and on the current day (Cost B ); formula (9) is the total cost of renewable energy power generation under the jurisdiction of the operator (Cost OM );

where:

The first stage constraints:

Equation (10) is the repulsion constraint of electric vehicle charging and discharging: the charging operation and discharging operation of the same electric vehicle cannot occur at the same time during the scheduling period;

Equation (11-14) is the electric vehicle charging state constraint: the electric vehicle is connected to the distribution network to start charging within the t period, in order to limit the shortest charging time and the shortest idle time, so as to avoid frequent switching between charging, discharging and idle states, resulting in Damaged EV batteries and higher costs of switching services; of which
for the shortest charging time,
is the shortest idle time;

Equation (15-18) electric vehicle discharge state constraint: used to limit the shortest discharge time and the shortest idle time; where
is the shortest discharge time;

Equation (19-21) Constraints on the maximum switching times of electric vehicle charging and discharging: the maximum number of electric vehicle charging and discharging switching times in a day is limited, and the maximum number of electric vehicle switching states in a day can be limited, which can effectively avoid excessive state switching of electric vehicles frequently; of which
are the upper limit of the charging and discharging times of a single electric vehicle in the V2G scheduling plan, respectively, and V i is the upper limit of the switching times of charging and discharging;

The second stage constraints:

Equation (22-23) The initial state constraints when electric vehicles are connected to the grid: Equation (22) calculates the initial power when the vehicle is connected to the grid through the travel distance before the electric vehicle is connected to the grid, and Equation (23) calculates the initial SOC of EV i , driving The randomness of the distance causes the initial SOC of the electric vehicle cluster to be random; where
is a random parameter of EV i ’s driving distance before grid connection in the protocol;

Equation (24-26) is the constraint on the maximum number of services with V2G nodes: due to the limitation of V2G service station capacity and transformer power, the number of vehicles that can be charged and discharged at the same node is limited; Equation (24) limits the maximum number of nodes that can be charged at the same time. Equation (25) limits the maximum number of electric vehicles that can be discharged at the same time at node m, and Equation (26) limits the number of electric vehicles that can be charged and discharged at the same time at node m less than the number of charging and discharging piles; where α m , β m are each The maximum number of vehicles that can be charged and discharged during the V2G service station period;

Equations (27-28) are the electric vehicle charging and discharging energy constraints: during the charging and discharging process of the electric vehicle, the actual chargeable and dischargeable amount is limited by the real-time SOC; among them: when
and
When both are 0, EV i is charged at time period t
and discharge capacity
is constrained to 0; when
or
, the charging capacity of EV i
and discharge capacity
Respectively subject to the maximum value of the schedulable capacity of the battery

and
constraint;

Equation (29-30) is the state-of-charge constraint of the electric vehicle: the variation range of the battery state of charge of the electric vehicle in the scheduling protocol is given; Equation (29) is the optimal battery operating condition range when the vehicle participates in V2G; Equation (30) It means that the state of charge SOC of the electric vehicle needs to meet the user's expectation after the scheduling, and the charging and discharging scheduling is carried out on the premise of meeting the user's future travel needs; where T end is set as the scheduling end time.

Equation (31) is the electric vehicle power balance constraint: the electric power of EV i in the t period is equal to the remaining electric power in the t-1 period plus the difference between the charge and discharge in the t period;

Equation (32-33) Electric vehicle charging and discharging climbing constraints: the charging and discharging climbing ability of electric vehicles is affected by the rated power of the charging and discharging piles and the charging mode. climb
Avoid battery charge and discharge exceeding the limit; if and only when the vehicle accepts the first-stage scheduling plan, the charge-discharge ramp constraint will take effect in the second stage;
The maximum hill-climbing capability for charging is,
The maximum ramping capability for discharge is;

Equations (34-35) are the maximum service capacity constraints of V2G nodes: Equation (35) calculates the total amount of charging demand generated by electric vehicles that arrive randomly outside the protocol; Equation (34) The capacity of electric vehicles in the protocol that can participate in charging scheduling, this part The amount of electricity is affected by the number of electric vehicles and the amount of charging outside the agreement of formula (35) and is random; among them
is the random arrival number of electric vehicles outside the agreement,
The rated charging capacity that node m can provide;

where:

Equations (36-37) are the network node capacity and balance constraints: the model builds an energy transmission network, and the node power balance of the network satisfies Kirchhoff’s law; Equation (36) limits the maximum capacity of the bidirectional energy flow, and stipulates that the power transmission is in the standard inside; formula (37) is introduced in
After describing the randomness of wind and solar power generation, construct an energy balance constraint for each node to ensure that the total inflow of the node is equal to the total outflow;

Linearization with nonlinear constraints:

Since both constraint equations (27) and (28) have nonlinear terms, equation (27) is transformed into equations (38)-(40), and equation (28) is transformed into equations (41)-(43) in the same way, In order to improve the quality and speed of the model solution set, where,